Map Editing System for GPS Path Identification
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Solution Overview
Problem
Conventional GPS-based navigation systems fail to accurately identify and visualize pedestrian and bicycle paths, as these paths are often not represented on digital or paper maps, leading to a lack of visibility and usability for users.
Innovation Solution
A map editing system that collects GPS data from multiple devices, filters it based on speed parameters to identify user classes, and displays the data on a map using visual representations, allowing users to create or modify paths by highlighting high-density areas of filtered samples, which can indicate pedestrian or bicycle paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional GPS-based navigation systems are used, then location tracking is achieved, but pedestrian and bicycle paths cannot be accurately identified or visualized
Solution Approach 1:
The system changes the parameter of data filtering by speed thresholds to differentiate between user classes (pedestrians, cyclists, motorists). By filtering GPS samples based on speed parameters, the system isolates pedestrian and bicycle paths from other traffic types, enabling accurate path identification that was previously impossible with conventional GPS systems.
Solution Approach 2:
The system introduces an intermediary processing layer that collects GPS samples from multiple devices, filters them by speed, and generates visual representations. This intermediary process transforms raw GPS data into meaningful path information, bridging the gap between location tracking and path visualization.
2Measurement precision
If GPS data from multiple devices is collected and filtered, then path identification accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the GPS data processing task into distinct stages: data collection from multiple devices, filtering by speed parameters, identification of high-density areas, and path creation. This segmentation simplifies the overall complexity by breaking down the complex processing into manageable, sequential steps.
Solution Approach 2:
The system employs automated algorithms that self-adjust to identify patterns in GPS data. The high-density area identification and path creation processes occur automatically based on the filtered samples, reducing the need for manual intervention and simplifying system operation despite the complex data processing involved.
3Manufacturing precision
If high-density areas are identified manually, then path creation accuracy improves, but user time and effort increase
Solution Approach 1:
The system performs preliminary actions by automatically identifying high-density areas and preparing path data before user interaction. This preliminary processing reduces the user's workload to simple verification and minor adjustments, maintaining high path creation accuracy while significantly reducing the time and effort required.
Solution Approach 2:
The system provides visual feedback by displaying high-density areas on the map, allowing users to verify the accuracy of automatically identified paths. This feedback mechanism enables users to quickly confirm or make minor corrections to path creations, maintaining high accuracy while minimizing the time invested.
Data Source
AI summary
A map editing system for visually indicating the location of GPS devices on a map and identifying paths from the map. GPS data is received from a plurality of GPS devices. The data is filtered based parameters including the speed of the GPS devices. The filtered GPS data is displayed on a map using representations that visually indicate the location of the GPS devices that the data was received from. The representations can then be used for identifying and creating new paths on the map.